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Author (up) Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados edit   pdf
url  doi
  Title End-to-End Handwritten Text Detection and Transcription in Full Pages Type Conference Article
  Year 2019 Publication 2nd International Workshop on Machine Learning Abbreviated Journal  
  Volume 5 Issue Pages 29-34  
  Keywords Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning  
  Abstract When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
  Address Sydney; Australia; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR WML  
  Notes DAG; 600.140; 601.311; 600.140 Approved no  
  Call Number Admin @ si @ CMV2019 Serial 3353  
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